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Skincare Recommendation MCP Server

Skincare Recommendation MCP Server

by AleWWH1104
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_products

Search a store catalog for skincare products that match your skin type and specific concerns, and filter to only in-stock items.

Instructions

Search the store's product catalog by skin type and/or skincare concern.

Args: skin_type: e.g. "grasa", "seca", "mixta", "sensible", "normal". Empty to skip this filter. concern: e.g. "acné", "manchas", "arrugas", "sensibilidad", "hidratación". Empty to skip this filter. in_stock_only: only return products currently in stock.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "search_products"
  }
}

Input Schema

NameRequiredDescriptionDefault
concernNo
skin_typeNo
in_stock_onlyNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral disclosure burden. It does disclose the return-oriented behavior of in_stock_only and the empty-string skip semantics, and 'Search' signals a read operation. Still, it does not mention result count, pagination, or any side-effect/permission profile, leaving some behavior implicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The one-sentence summary is front-loaded, followed by a compact Args block. Each line adds a specific example or behavior, so there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 3-parameter read-only search with an output schema present, the definition provides enough invocation detail: all params explained, defaults in schema, return shape in output schema. It only lacks sibling-selection guidance and a note on result limits, so 4.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must define the parameters, and it does. skin_type and concern include concrete example values and the explicit 'Empty to skip this filter' instruction, and in_stock_only is defined as 'only return products currently in stock'. This covers all three parameters with meaning beyond the schema's names and defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific action ('Search') and resource ('the store's product catalog'), and narrows scope with 'by skin type and/or skincare concern'. This makes the operation clear, but it never names sibling tools or states how it differs from them, so it stays at 4.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The Args section tells callers exactly which filters are available and how to skip each one, but there is no explicit when-to-use or when-not-to-use guidance relative to siblings like get_product_details, find_alternatives, or recommend_products. Usage is implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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